In recent years, dramatic growth in various wired and wireless communication applications has led to attendant increases in energy consumption. Thus, it is not surprising that communications developers investigate radio and networking solutions, which are energy-efficient and resource-efficient. One emerging wireless technology, Cognitive Radio (CR), can facilitate improved spectrum usage and efficiency.
For example, by intelligently monitoring the spectrum, Secondary Users (SUs) can opportunistically access the idle spectrum originally assigned to Primary Users (PUs). On the other hand, such new functionalities and additional tasks (e.g., spectrum sensing) of CR enabling devices can require an amount of energy to provide such benefits. Nevertheless, with the associated agility and intelligence, CR technology can create new possibilities and methods to realize are energy-efficient communications and energy-efficient Cognitive Radio Networks (CRNs).
Thus, improvements in the designs of protocols for opportunistic spectrum access can be expected to provide further opportunities for improvements in energy-efficiency. For instance, a separation principle for a joint design problem of spectrum sensor operating points, sensing channel selection, and access policy utilized has been studied, and a simple but robust round-robin myopic channel selection policy held for a general number of positive correlated channels has been offered, while another approach extended techniques to the imperfect sensing case.
However, prior approaches of cooperative sensing and various conventional schemes to fuse sensing information from SUTs in the framework of a Partially Observable Markov Decision Process (POMDP) focused on improving sensing performance, such as sensing performance on a single channel, while neglecting the problem of how to assign SUTs to sense multiple channels, e.g., the Cooperative Sensing Scheduling (CSS) problem. As examples, the impact of the cooperative sensing overhead on the system throughput has been studied with the consideration of the number of reporting packets, and the tradeoff of finding the optimal sensing time and the parameter for the result fusion in order to maximize SUTs' throughput was characterized. Further works have extended the analysis to the case of multiple channels using a soft decision fusion rule.
Thus, the determination of how to assign SUTs to sense multiple channels, e.g., the CSS problem, remains largely unexplored. For example, conventional approaches are commonly formulated as a static optimization problem, rather than considering the CSS problem under varying spectrum environments with uncertainty (e.g., rather than being analyzed as a dynamic CSS problem), which can result in a sequential decision problem as described elsewhere. For instance, the CSS problem can be considered to be a non-deterministic polynomial-time hard optimization problem (e.g., an NP-hard problem).
However, conventional approaches to the problem apply numerical methods to obtain a CSS solution, which are applied to impractical or theoretical cases (e.g., where only the probability of detection is considered in the numerical solution, etc.), using certain assumptions or methods (e.g., a soft result fusion method, etc.) that can result in additional computational complexity and overhead, and without a direct focus on an energy-efficiency objective in the optimization problem. In addition, some conventional approaches that treat the CSS problem as a static optimization problem can impose strictures on the solution (e.g., fixed number of channels that can be sensed in each slot, fixed sensing duration in each slot, etc.).
The above-described deficiencies are merely intended to provide an overview of some of the problems encountered in CR design, and are not intended to be exhaustive. Other problems with conventional systems and corresponding benefits of the various non-limiting embodiments described herein may become further apparent upon review of the following description.